Project Summary Waterborne transmission of human pathogens is responsible for up to 19 million cases of gastroenteritis in the US each year and 2.2 million deaths globally, primarily affecting children under five years old. Fecal pollution introduces pathogens into surface and groundwater used for drinking water, household needs, and recreation. Traditional indicators of fecal pollution Escherichia coli (E. coli) and enterococci lack specificity for host source (animal or human) and generally correlate poorly with the presence of disease-causing organisms. Untreated sewage has a high likelihood of carrying human pathogens; therefore, new alternative indicators that are specific for human sources would be a more reliable assessment of human health risk. We have generated high-resolution 16S rRNA gene profiles of microbial communities in sewage from 71 US cities and multiple animal species, which identified more than 65 candidates for alternative indicators of human fecal pollution. In this work, we will pinpoint the most sensitive and specific indicators for human fecal pollution and develop a suite of quantitative assays that will be rigorously validated for sensitivity and specificity. We will draw upon the novel computational tools we have developed to create a sequence classification pipeline that will allow users to identify fecal pollution sources from sequence data. We will generate empirical data for relationships between pathogens and new indicators in untreated sewage to use in a quantitative microbial risk assessment (QMRA) framework, which would inform guidelines for concentrations of human specific indicators that relate to acceptable risk thresholds following exposure. The ecology of indicators and pathogens will be compared in a defined watershed. We will follow the decay of pathogens and indicators during sewage overflows in a water mass (plume) as it disperses in Lake Michigan. We will also leverage a large study where pathogen concentrations have been measured at multiple upstream river sites. Many factors influence sewage indicators and pathogen relationships at the watershed scale. We will use logistic and multiple linear regression to examine these factors (number of people contributing to sewage contamination, disease incidence in the community), as well as indicator concentrations and hydrological parameters, to determine predictors for pathogen concentrations. These calculations will shed light on instances where the ecology of indicators and pathogens might be different. Importantly, this project will provide training for a diverse body of undergraduates, graduate students, and postdocs within an interdisciplinary environment that is at the crossroads of microbial ecology, genetics, microbiology, hydrology, infectious disease, and public health.